Virtual Sensor Data Generation For Wheel Stop Detection

ABSTRACT

The disclosure relates to methods, systems, and apparatuses for virtual sensor data generation and more particularly relates to generation of virtual sensor data for training and testing models or algorithms to detect objects or obstacles. A method for generating virtual sensor data includes simulating, using one or more processors, a three-dimensional (3D) environment comprising one or more virtual objects. The method includes generating, using one or more processors, virtual sensor data for a plurality of positions of one or more sensors within the 3D environment. The method includes determining, using one or more processors, virtual ground truth corresponding to each of the plurality of positions, wherein the ground truth comprises a dimension or parameter of the one or more virtual objects. The method includes storing and associating the virtual sensor data and the virtual ground truth using one or more processors.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application is a continuation of and claims the benefit of andpriority to U.S. patent application Ser. No. 14/975,177, entitled“Virtual Sensor Data Generation For Wheel Stop Detection”, filed Dec.18, 2015 by Ashley Elizabeth Micks et al., the entire contents of whichare expressly incorporated by reference.

TECHNICAL FIELD

The disclosure relates generally to methods, systems, and apparatusesfor virtual sensor data generation and more particularly relates togeneration of virtual sensor data for training and testing models oralgorithms to detect objects or obstacles, such as wheel stops orparking barriers.

BACKGROUND

Automobiles provide a significant portion of transportation forcommercial, government, and private entities. Due to the high value ofautomobiles and potential harm to passengers and drivers, driver safetyand avoidance of accidents or collisions with other vehicles or objectsare extremely important.

BRIEF DESCRIPTION OF THE DRAWINGS

Non-limiting and non-exhaustive implementations of the presentdisclosure are described with reference to the following figures,wherein like reference numerals refer to like parts throughout thevarious views unless otherwise specified. Advantages of the presentdisclosure will become better understood with regard to the followingdescription and accompanying drawings where:

FIG. 1 is a schematic block diagram illustrating an implementation of avehicle control system that includes an automated driving/assistancesystem;

FIG. 2 is a schematic block diagram illustrating an implementation of asystem for sensor data generation;

FIG. 3 is a schematic diagram illustrating a side view of a vehiclelocated near a parking chock;

FIG. 4 is a schematic top view diagram illustrating a virtual parkinglot environment, according to one embodiment;

FIG. 5 is an example frame of sensor data;

FIG. 6 is an example complimentary frame of the frame illustrated inFIG. 5, according to one implementation;

FIG. 7 is a schematic block diagram illustrating example components of asimulation component, according to one implementation; and

FIG. 8 is a schematic flow chart diagram illustrating a method forgenerating virtual sensor data, according to one implementation.

DETAILED DESCRIPTION

A very common location for collisions or accidents for vehicles occursduring parking or leaving a parking location. Because of the close rangeof other vehicles, pedestrians, or other objects, there tends to be asmall margin for error if damage to vehicles, including scrapes tobumpers or side panels, is to be avoided. In many parking lots, or atother parking locations, parking stalls are marked or bounded on atleast one side by some sort of parking barrier, such as a parking chock,curb, or the like. The parking barriers may be used to keep vehiclesfrom rolling too far forward (or backward) into another row of vehiclesand may stop a vehicle from moving too far when the wheels encounter thebarrier. A parking barrier may include, or be referred to herein as, aparking barrier, a parking chock, a parking wheel stopper, a curb, orthe like.

When a car is parked in a parking lot, it may come across a wheel stopor other parking barrier. Applicants have recognized that the sizes andheights of wheel stops can vary significantly. In some situations, adriver, automated driving system, or automated assistance system is notable to detect the wheel stop, and the vehicle may run over it anddamage the front or rear bumper or fascia. In order to avoid such acollision, both the position and the height of the wheel stop must beknown so that bumping into or scraping the bottom of the car can beavoided.

However, Applicants have recognized that if wheel stops or parkingbarriers are to be detected by onboard perception, so that a warning canbe provided to the driver, the automated driving system, or theautomated assistance system or so that autonomous braking can beinitiated as needed, the detection algorithms will need to be trainedand tested on large amounts of diverse data. However, real world sensordata takes considerable time and resources to acquire, by setting upphysical tests or driving around with sensors to collect data forrelevant scenarios.

The present application discloses systems and methods for generation ofsynthetic or virtual sensor data and/or associated ground truth. In oneembodiment, a method includes simulating a three-dimensional (3D)environment that includes one or more parking barriers. The methodincludes generating virtual sensor data for a plurality of positions ofone or more sensors within the 3D environment. The method furtherincludes determining virtual ground truth corresponding to each of theplurality of positions, wherein the ground truth includes a height ofthe at least one of the parking barriers. The method further includesstoring and associating the virtual sensor data and the virtual groundtruth.

Some implementations integrate a virtual driving environment, createdusing 3D modeling and animation tools, with sensor models to producevirtual sensor data in large quantities in a short amount of time.Relevant parameters, such as lighting and positioning of the wheel stop,may be randomized in the recorded data to ensure a diverse dataset withminimal bias. In one implementation, a sensor is positioned relative toa roadway or parking lot (or other virtual driving environment)according to its planned positioning on a vehicle. The virtual sensormay be repositioned and/or moved along a virtual path in the virtualenvironment into locations where it can observe objects or obstacles,such as wheel stops.

During virtual movement or repositioning, the virtual sensor mayperiodically record data or generate virtual sensor data. For each timestep of recorded data—each frame of camera data, for example—annotationsare automatically provided to record ground truth information about thepositions of all wheel stops within range of the sensor as well as theheight of each wheel stop. In the case of camera data, ground truth datamay include, for each frame of image data, a complimentary frame ofpixel-wise segmented image data showing the same view. For example, allof the pixels of the wheel stop in the complementary frame may be asolid color with constant values (e.g., red-green-blue (RGB)) so that itis known exactly which pixels belong to the wheel stop. The ground truthinformation may be used to train a perception algorithm using supervisedlearning, or to test existing algorithms and quantify their performance.The embodiments may generate virtual sensor for a camera, a lightranging and detection (LIDAR) system, a radar system, an ultrasoundsystem, and/or a different sensor system or sensor type.

The systems and methods disclosed herein may provide significantbenefits over real-world data. For example, compared to real-world data,virtual data is cheaper in terms of time, money, and resources.Specifically, systems and methods disclosed herein may generatethousands of virtual images for a variety of conditions within a fewminutes as opposed to hours or months acquiring a similar number ofreal-world images. The virtual sensor data with automatic annotationsgreatly improves ease in obtaining ground truth or other data useful fortraining and testing object detection or localization, such as for wheelstop detection algorithms.

Although some embodiments of the present disclosure discuss simulationsand virtual data for detection, classification, and/or dimensiondeterminations of parking barriers, these are given by way of exampleonly. The present disclosure contemplates the use of the systems,methods, and devices disclosed herein for the detection, classification,localization, and dimension detection for any object or obstacle. Forexample, virtual representations of any object or obstacle that may bedetected by a vehicle in a driving environment are contemplated herein.

Referring now to the figures, FIG. 1 illustrates an example vehiclecontrol system 100 that may be used to automatically detect parkingbarriers. The automated driving/assistance system 102 may be used toautomate or control operation of a vehicle or to provide assistance to ahuman driver. For example, the automated driving/assistance system 102may control one or more of braking, steering, acceleration, lights,alerts, driver notifications, radio, or any other auxiliary systems ofthe vehicle. In another example, the automated driving/assistance system102 may not be able to provide any control of the driving (e.g.,steering, acceleration, or braking), but may provide notifications andalerts to assist a human driver in driving safely. The automateddriving/assistance system 102 may use a neural network, or other modelor algorithm to determine that a parking barrier or chock is present andmay also determine a size, location, and/or dimensions of an object orobstacle, such as a parking barrier or chock.

The vehicle control system 100 also includes one or more sensorsystems/devices for detecting a presence of nearby objects ordetermining a location of a parent vehicle (e.g., a vehicle thatincludes the vehicle control system 100). For example, the vehiclecontrol system 100 may include one or more radar systems 106, one ormore LIDAR systems 108, one or more camera systems 110, a globalpositioning system (GPS) 112, and/or one or more ultra sound systems114. The vehicle control system 100 may include a data store 116 forstoring relevant or useful data for navigation and safety, such as mapdata, driving history, or other data. The vehicle control system 100 mayalso include a transceiver 118 for wireless communication with a mobileor wireless network, other vehicles, infrastructure, or any othercommunication system.

The vehicle control system 100 may include vehicle control actuators 120to control various aspects of the driving of the vehicle, such aselectric motors, switches or other actuators, to control braking,acceleration, steering or the like. The vehicle control system 100 mayalso include one or more displays 122, speakers 124, or other devices sothat notifications to a human driver or passenger may be provided. Adisplay 122 may include a heads-up display, dashboard display orindicator, a display screen, or any other visual indicator, which may beseen by a driver or passenger of a vehicle. The speakers 124 may includeone or more speakers of a sound system of a vehicle or may include aspeaker dedicated to driver notification.

It will be appreciated that the embodiment of FIG. 1 is given by way ofexample only. Other embodiments may include fewer or additionalcomponents without departing from the scope of the disclosure.Additionally, illustrated components may be combined or included withinother components without limitation.

In one embodiment, the automated driving/assistance system 102 isconfigured to control driving or navigation of a parent vehicle. Forexample, the automated driving/assistance system 102 may control thevehicle control actuators 120 to drive a path on a road, parking lot,driveway, or other location. For example, the automateddriving/assistance system 102 may determine a path based on informationor perception data provided by any of the components 106-118. The sensorsystems/devices 106-110 and 114 may be used to obtain real-time sensordata so that the automated driving/assistance system 102 can assist adriver or drive a vehicle in real-time. The automated driving/assistancesystem 102 may implement an algorithm or use a model, such as a deepneural network, to process the sensor data and identify a presence,location, height, and/or dimension of a parking barrier, object, orother obstacle. However, in order to train or test a model or algorithm,large amounts of sensor data may be needed.

Referring now to FIG. 2, one embodiment of a system 200 for sensor datageneration is shown. The system 200 includes a simulation component 202,storage 204, a training component 206, and a testing component 208. Thesimulation component 202 may be configured to simulate a drivingenvironment and generate virtual sensor data 210 and virtual groundtruth or other information as annotations 212 for the virtual sensordata 210. The annotations may include any type of ground truth, such assimulation conditions used by the simulation component 202 to generatethe driving environment and/or virtual sensor data 210. For example, thevirtual ground truth may include a virtual distance between a sensor anda virtual parking barrier, one or more dimensions of the virtual parkingbarrier (e.g., the height), or any other object or obstacle. Similarly,the virtual ground truth may include one or more details about lightingconditions, weather conditions, sensor position, sensor orientation,sensor velocity, and/or virtual sensor type (e.g., a specific model ofsensor). The simulation component 202 may annotate frames or sets ofvirtual sensor data with the corresponding ground truth or store thevirtual ground truth with an indication of the sensor data to which thevirtual ground truth belongs.

The virtual sensor data 210 and/or any information for inclusion inannotations 212 may be stored in storage 204. Storage 204 may includelong term storage such as a hard disk or machine storage such as randomaccess memory (RAM). The virtual sensor data 210 and any associatedannotations 212 may be stored as part of the same file or may be storedin separate files. The training component 206 and/or the testingcomponent 208 may then access and use the virtual sensor data 201 and/orannotations 212 for training or testing a parking barrier detectionalgorithm or model.

The training component 206 is configured to train a machine learningalgorithm using virtual sensor data and ground truth generated by thesimulation component 202. For example, the training component 206 maytrain a machine learning algorithm or model by providing at least aportion of the virtual sensor data and corresponding virtual groundtruth to train the machine learning algorithm or model to determine oneor more of a height and a position of the one or more parking barriers,object, or other obstacle. The training component 206 may provide thevirtual sensor data and virtual ground truth to a training algorithm fora neural network. For example, the training component 206 may train aneural network using one frame of sensor data and associated groundtruth at a time. In one embodiment, the training component 206 may traina plurality of different machine learning models to identify differentaspects of virtual sensor data. For example, one model may be used toclassify an object in a virtual sensor frame as a parking barrier, whileanother one or more other models may be used to determine a position,orientation, distance, and/or dimension of the parking barrier, object,or other obstacle.

The testing component 208 may test a machine learning algorithm or modelusing the virtual sensor data and virtual ground truth. For example, thetesting component 208 may provide at least a portion of the virtualsensor data to the machine learning algorithm or model to determine oneor more of a height and a position of the parking barrier, object, orother obstacle and compare a determined height or a determined positionwith the virtual ground truth. The testing component 208 may be able toaccurately determine how well a model or algorithm performs because adetermined classification or value may be compared with the virtualground truth. If an algorithm or model is sufficiently accurate, it maybe implemented as part of an automated driving/assistance system 102.

FIG. 3 illustrates a side view of a vehicle 302 and a parking barrier304. If the vehicle 302 does not stop short of the parking barrier 304,the parking barrier 304 may impact, scrape, or damage a bumper or otherportion of the vehicle 302. As the vehicle moves closer in proximity,the parking barrier 304 may not be visible through a windshield of thevehicle 302 and one or more sensors mounted on or near the hood or roofof the vehicle 302 also may not be able to detect the parking barrier304. Thus, it may be important to obtain an accurate height and locationof the parking barrier 304, while the parking barrier is detectable sothat contact between the vehicle 302 and the parking barrier 304 can beavoided when the vehicle is parked or otherwise driven near the parkingbarrier 304. Additionally, the location of the parking barrier 304 maybe stored in memory so that when a driver returns or the vehicle 302 isrestarted, the driver or automated driving/assistance system 102 can bereminded of the presence and location of the parking barrier 304.

FIG. 4 is a top view diagram of a virtual parking lot environment 400with a plurality of parking locations and parking barriers 404. Forexample, the virtual parking lot environment 400 may be generated andsimulated by the simulation component 202. Thus, the virtual parking lotenvironment 400, and all objects, may represent computer models orsimulations that are generated by the simulation component 202. Thevirtual parking lot environment 400 may also include one or morelighting sources or other objects to simulate a real parking lotenvironment. For example, the lighting may simulate different times ofday, various weather types, and/or lighting positions. One or moreplants, curbs, or a surrounding environment for the virtual parking lotenvironment 400 may also be generated or simulated.

Within the virtual parking lot environment 400, a vehicle 402 is shownas it is approaching or pulling into a specific parking stall having aparking barrier 404. The parking barrier 404 includes a parking chock.Embodiments of parking chocks include concrete, rubber, or otherbarriers, which are placed at parking locations to prevent vehicles frompulling or rolling too far. The virtual parking lot environment 400includes a parking barrier 404 for each parking stall. However, someparking lots may be inconsistent with inclusion of parking barriers sothat a driver or system cannot assume that a specific parking stall doesor does not include a parking barrier based on other parking stalls.According to one embodiment, as indicated by line 406, as the vehicle402 pulls into the parking stall one or more sensors may obtain data(such as an image or frame) that includes a portion of the parkingbarrier 404. As discussed above, the simulation component 202 maygenerate a virtual frame of perception data that simulates what a sensoron the vehicle 402 would capture if the virtual parking lot environment400 was a real-world environment.

In one embodiment, the vehicle 402 (with any associated virtual sensors)is moved along a path or randomly repositioned within the virtualparking lot environment 400 and additional frames of perception data maybe generated. Similarly, information corresponding to the simulatedconditions may be saved with the frames so that virtual ground truth isavailable for each frame. In one embodiment, positions of objects withinthe virtual parking lot environment 400 and one or more other conditionsmay be randomized for a plurality of different frames. For example,lighting, positions, weather conditions, or the like may be randomlygenerated, within acceptable bounds, to generate virtual sensor data fora wide array of different conditions.

Although FIG. 4 illustrates a plan view of a virtual parking lotenvironment 400, vehicles may be parked in a wide variety of locationswhere parking barriers 404 or parking chocks may be present. Forexample, road-side parking, driveway parking, or any other parkinglocation may also include a parking barrier, parking chock, wheel stop,curb, or other object to define parking locations or parking stalls.Thus, any location where vehicles are driven or may be parked may besimulated in one or more other virtual environments.

FIG. 5 illustrates an example frame 500 of sensor data generated by asimulation component 202. For example, the frame 500 may include avirtual image captured by a virtual camera located at a simulatedposition within a virtual environment. The frame 500 includes a parkingbarrier 502 positioned within a virtual environment. The shape andposition of the parking barrier 502 within the frame 500 may be a resultof the current position and orientation of the parking barrier 502 aswell as a virtual camera that has “captured” the frame 500. Virtualground truth for the frame 500 may be saved with the frame 500 or may beassociated with the frame 500 so that specific virtual conditions forthe frame 500 are known. The virtual ground truth may include a distance(e.g., a simulated distance in feet, meters, or other measurement unit)between a sensor and the parking barrier 502, an orientation of thesensor, an orientation of the parking barrier 502, one or moredimensions of the parking barrier 502, a material of the parking barrier502, specific positions of both the parking barrier 502 and a sensorthat capture the frame 500, simulated weather conditions, simulated timeof day, simulated lighting positions, simulated lighting colors, or anyother additional information about a simulated environment in which theframe 500 was captured.

FIG. 6 illustrates one embodiment of a complimentary frame 600corresponding to the frame 500 of FIG. 5. The complimentary frameincludes a region 602 of a solid color that corresponds to a region ofthe frame 500 where the pixels of the parking barrier 502 are located.In FIG. 6, the region 602 is white, while the rest of the complimentaryframe 600 is black. However, other embodiments may be similar to theoriginal image with a solid color covering a region of the parkingbarrier 602. For example, a bright green color may be used for theregion 602, while the black portion of the complimentary frame 600 maynot be black, but may be identical to the corresponding regions/pixelsof the original frame 500. In one embodiment, the complimentary frame600 may be included in ground truth information for the frame 500 sothat an algorithm may be trained or tested. For example, thecomplementary frame 600 may be provided with the frame 500 for trainingof a neural network that is used to detect and/or identify dimensions ofa parking barrier.

Although FIG. 5 and FIG. 6 are discussed above in relation to cameraimages, other types of sensor data frames are contemplated and fallwithin the scope of the present disclosure. For example, LIDAR frames,radar frames, ultrasound frames, or any other type of sensor data framemay be generated and stored within any simulated ground truth.Additionally, although some embodiments and examples provided hereininclude the simulation and modeling of parking barriers, any other typeof object or data may be used. For example, virtual sensor data for anytype of object that may be encountered in a driving environment may begenerated. Example objects or obstacles may include parking barriers orcurbs, other vehicles, road or lane lines, parking lines, road signs,pedestrians, cyclists, animals, road debris, bumps or dips in a road, orany other object, obstacle or feature, which may alter how a vehicleshould operate or alter a path of a vehicle.

FIG. 7 is a block diagram illustrating example components of asimulation component 202. In the depicted embodiment, the simulationcomponent 202 includes an environment component 702, a virtual sensorcomponent 704, a ground truth component 706, a storage component 708,and a model component 710. The components 702-710 are given by way ofillustration only and may not all be included in all embodiments. Infact, some embodiments may include only one or any combination of two ormore of the components 702-710. Some of the components 702-710 may belocated outside the simulation component 202.

The environment component 702 is configured to generate and/or simulatea virtual environment. In one embodiment, the environment component 702simulates or generates a 3D parking or driving environment. Theenvironment component 702 may use a 3D gaming or simulation engine forcreating, simulating, and/or rendering an environment where a vehiclemay be driven or parked. For example, gaming engines used for drivinggames or any other game design may be used for purposes of simulating areal-world environment.

In one embodiment, the environment component 702 simulates anenvironment with a plurality of virtual objects. The virtual objects mayinclude parking barriers, vehicles, trees, plants, curbs, painted lines,buildings, landscapes, pedestrians, animals, or any other objects thatmay be found in a driving or parking environment. The environmentcomponent 702 may simulate crowded conditions where there are a largenumber of vehicles, pedestrians, or other objects. The environmentcomponent 702 may also simulate lighting conditions. For example, theenvironment component 702 may simulate a light source including a sun,moon light, street lights, building lights, vehicle headlights, vehiclebrake lights, or any other light source. The environment component 702may also simulate shadows, lighting colors for the sun or moon atdifferent times of the day, or weather conditions. For example, theenvironment component 702 may simulate lighting for cloudy, rainy,snowy, and other weather conditions. Additionally, the environmentcomponent 702 may simulate wet or snow conditions where roads, parkinglots, and objects in a virtual environment are wet or covered with snow.

In one embodiment, the environment component 702 may randomize simulatedconditions. For example, the environment component 702 may periodicallyrandomize one or more simulated conditions to generate environmentshaving a wide array of conditions. In one embodiment, the environmentcomponent 702 may randomly generate different conditions for one or moreof lighting, weather, a position of the one or more virtual parkingbarriers or other objects, and dimensions of the one or more virtualparking barriers or other objects.

In one embodiment, the environment component 702 may simulate a positionof a sensor within the virtual environment. The environment component702 may simulate movement of one or more sensors along a path within thevirtual environment or may randomize sensor positioning. For example,the environment component 702 may simulate a position and/or orientationof a sensor based on a planned location on a vehicle. In one embodiment,the environment component 702 may randomize a position, height,orientation, or other positioning aspects of a sensor within the virtualenvironment. The randomized locations for the sensor, or other simulatedconditions for the virtual environment may be randomized withinpredefined bounds to increase likelihood that the virtual environment issimilar to conditions that would be encountered by vehicles inreal-world situations.

The virtual sensor component 704 is configured to generate sensor dataor perception data for a virtual sensor within a virtual environmentgenerated or simulated by the environment component 702. In oneembodiment, the virtual sensor component 704 may include or use a modelof real-world performance of one or more specific sensors that are to beused by a vehicle. For example, a sensor may have a virtual model thatsimulates the real-world performance of the sensor. The virtual sensorcomponent 704 may simulate how a sensor generates a frame. The virtualsensor component 704 may generate virtual sensor data that includes oneor more of computer generated images, computer generated radar data,computer generated LIDAR data, computer generated ultrasound data, orother data for other types of perception sensors.

In one embodiment, the virtual sensor component 704 is configured togenerate sensor frames or sensor data on a periodic basis. For example,the virtual sensor component 704 may generate an image (or other sensor)at a simulated interval similar to frequently a camera captures animage. In one embodiment, the virtual sensor component 704 createssensor data for each position simulated by the environment component702. For example, the virtual sensor component 704 may generate sensordata for positions along a path traveled by a virtual vehicle within avirtual environment. In one embodiment, one or more of the images orframes of sensor data include a portion of a virtual parking barrier orother object. For example, computer generated images of parking barriersor other objects in a virtual environment may be produced by the virtualsensor component 704.

The ground truth component 706 is configured to generate virtual groundtruth for the virtual sensor data generated by the virtual sensorcomponent 704. For example, the ground truth component 706 may determinesimulated conditions for each image or frame captured by the virtualsensor component 704. In one embodiment, the environment component 702may provide the simulated conditions to the ground truth component 706.The ground truth component 706 may select one or more simulatedconditions as ground truth or calculate ground truth based on thesimulated conditions for specific virtual sensor data. For example, theground truth component 706 may select a dimension of a parking barrier(such as height) as ground truth for a computer generated image orframe. As another example, the ground truth component 706 may receivevirtual positions of a parking barrier and a sensor and then calculate avirtual distance (e.g., line of sight distance and/or horizontaldistance) between the virtual sensor and the parking barrier. Similarinformation about other objects or obstacles within the virtualenvironment is also contemplated.

The virtual ground truth may include information about a position andorientation of a sensor, a position and orientation of a parking barrieror other object, one or more dimensions of a parking barrier or otherobject, lighting conditions, weather conditions, a distance between thesensor and the parking barrier or other object, a type of sensor used tocapture sensor data, or any other information about simulationconditions. In one embodiment, a uniform set of ground truth may bedetermined for each frame or set of sensor data generated by the virtualsensor component 704. For example, the same ground truth information(e.g., sensor height, distance, etc.) for each position where virtualsensor data was generated may be computed.

In one embodiment, the ground truth component 706 may generate acomplementary frame for a frame of sensor data generated by the virtualsensor component 704 (see FIG. 6). For example, the complementary framemay have the same color value for pixels corresponding to the one ormore virtual parking barriers. For example, each pixel corresponding toa virtual parking barrier may have the same color so that a trainingalgorithm or a testing algorithm can clearly determine what portion ofvirtual sensor data corresponds to a virtual parking barrier. In oneembodiment, each pixel of the complementary frame may include an imagepixel, radar or LIDAR vector, or other pixel or matrix value of virtualsensor data.

The storage component 708 is configured to store the virtual sensor datagenerated by the virtual sensor component 704 and/or any ground truthdetermined by the ground truth component 706. For example, the storagecomponent 708 may store the virtual sensor data and/or ground truth inthe storage 204 of FIG. 2. In one embodiment, the storage component 708may associate or annotate the virtual sensor data with correspondingground truth or other information about simulated conditions. The sensordata and ground truth may then be used for a wide variety of purposes,such as for training a machine learning algorithm or model or fortesting a machine learning algorithm or model.

The model component 710 is configured to provide the virtual sensor dataand/or ground truth to an algorithm for testing or training of a machinelearning algorithm or model. For example, the model component 710 mayprovide the virtual sensor data and/or the ground truth provided by thevirtual sensor component t704 and/or ground truth component 706 to thetraining component 206 or testing component 208 of FIG. 2. In anotherembodiment, the model component 710 may include the training component206 and/or the testing component 208. For example, the virtual sensordata and/or virtual ground truth may be used to train or test a neuralnetwork, deep neural network, and/or convolution neural network fordetecting, identifying, determining one or more properties of a parkingbarrier or other object. For example, the machine learning algorithm ormodel may be trained or tested for inclusion in the automateddriving/assistance system 102 of FIG. 1.

Referring now to FIG. 8, a schematic flow chart diagram of a method 800for generating virtual sensor data and ground truth is illustrated. Themethod 800 may be performed by simulation component or a system forsensor data generation, such as the simulation component 202 of FIG. 2or 7 or the system 200 for sensor data generation of FIG. 2.

The method 800 begins and an environment component 702 simulates athree-dimensional (3D) environment comprising one or more parkingbarriers or other objects at 802. A virtual sensor component 704generates virtual sensor data for a plurality of positions of one ormore sensors within the 3D environment at 804. A ground truth component706 determines virtual ground truth corresponding to each of theplurality of positions at 806. The ground truth may include informationabout at least one object within the virtual sensor data, such as anobject with one or more features captured in an image or other sensordata. The information may include any information about the objectsdiscussed herein, such as dimensions, position, or orientations ofobjects. For example, the ground truth may include a height of the atleast one of the parking barriers or other objects. A storage component708 stores and associates the virtual sensor data and the virtual groundtruth at 808. The method may also include a model component 710providing a the virtual sensor data and/or virtual ground truth to atraining component 206 or a testing component 208 of FIG. 2 for trainingor testing of a machine learning algorithm or model. After trainingand/or testing a model, such as a deep neural network, the model may beincluded in the vehicle control system 100 of FIG. 1 for active objector parking barrier detection and dimension estimation during real-worlddriving conditions.

EXAMPLES

The following examples pertain to further embodiments.

Example 1 is a method that includes simulating a 3D environment thatincludes one or more objects, such as parking barriers. The methodincludes generating virtual sensor data for a plurality of positions ofone or more sensors within the 3D environment. The method includesdetermining virtual ground truth corresponding to each of the pluralityof positions. The ground truth includes information about at least oneobject within the sensor data. For example, the ground truth may includea height of the at least one of the parking barriers. The method alsoincludes storing and associating the virtual sensor data and the virtualground truth.

In Example 2, the method of Example 1 further includes providing one ormore of the virtual sensor data and the virtual ground truth fortraining or testing of a machine learning algorithm or model.

In Example 3, the machine learning model or algorithm in Example 2includes a neural network.

In Example 4, training the machine learning algorithm or model in any ofExamples 2-3 includes providing at least a portion of the virtual sensordata and corresponding virtual ground truth to train the machinelearning algorithm or model to determine one or more of a height and aposition of one or more parking barriers or other objects.

In Example 5, testing the machine learning algorithm or model in any ofExamples 2-4 includes providing at least a portion of the virtual sensordata to the machine learning algorithm or model to determine aclassification or a position of at least one object and compare theclassification or the position with the virtual ground truth.

In Example 6, the plurality of positions in any of Examples 1-5correspond to planned locations of sensors on a vehicle.

In Example 7, the virtual sensor data in any of Examples 1-6 includesone or more of computer generated images, computer generated radar data,computer generated LIDAR data, and computer generated ultrasound data.

In Example 8, simulating the 3D environment in any of Examples 1-7includes randomly generating different conditions for one or more oflighting, weather, a position of the one or more objects, and aclassification or type of the one or more objects.

In Example 9, generating the virtual sensor data in any of Examples 1-8includes periodically generating the virtual sensor data duringsimulated movement of the one or more sensors within the 3D environment.

In Example 10, determining the virtual ground truth in any of Examples1-9 includes generating a ground truth frame complimentary to a frame ofvirtual sensor data, wherein the ground truth frame includes a samecolor value for pixels corresponding to the one or more objects.

Example 11 is a system that includes an environment component, a virtualsensor component, a ground truth component, and a model component. Theenvironment component is configured to simulate a 3D environmentcomprising one or more virtual objects or obstacles. The virtual sensorcomponent is configured to generate virtual sensor data for a pluralityof positions of one or more sensors within the 3D environment. Theground truth component is configured to determine virtual ground truthcorresponding to each of the plurality of positions, wherein the groundtruth includes information about at least one obstacle of the one ormore obstacles. The model component is configured to provide the virtualperception data and the ground truth to a machine learning algorithm ormodel to train or test the machine learning algorithm or model.

In Example 12, the model component in Example 11 is configured to trainthe machine learning algorithm or model, wherein training includesproviding at least a portion of the virtual sensor data andcorresponding virtual ground truth to train the machine learningalgorithm or model to determine a classification or position of the atleast one obstacle.

In Example 13, the model component in any of Examples 11-12 isconfigured to test the machine learning algorithm or model. The testingincludes providing at least a portion of the virtual sensor data to themachine learning algorithm or model to determine a classification orposition of the at least one obstacle or object and comparing theclassification or the position with the virtual ground truth.

In Example 14, the virtual sensor component in any of Examples 11-13 isconfigured to generate virtual sensor data comprising one or more ofcomputer generated images, computer generated radar data, computergenerated LIDAR data, and computer generated ultrasound data.

In Example 15, the environment component in any of Examples 11-14 isconfigured to simulate the 3D environment by randomly generatingdifferent conditions for one or more of the plurality of positions,wherein the different conditions comprise one or more of: lightingconditions; weather conditions; a position of the one or more obstaclesor objects; and dimensions of the one or more obstacles or objects.

Example 16 is a computer readable storage media storing instructionsthat, when executed by one or more processors, cause the one or moreprocessors to generate virtual sensor data for a plurality of sensorpositions within a simulated 3D environment comprising one or morevirtual objects. The instructions cause the one or more processors todetermine one or more simulated conditions for each of the plurality ofpositions, wherein the simulated conditions comprise one or more ofclassification, a position, and a dimension of at least one object ofthe one or more objects. The instructions cause the one or moreprocessors to store and annotate the virtual sensor data with thesimulated conditions.

In Example 17, the instructions in Example 16 further cause the one ormore processors to train or test a machine learning algorithm or modelbased on one or more of the virtual sensor data and the simulatedconditions.

In Example 18, the instructions in any of Examples 16-17 further causethe processor to one or more of: train the machine learning algorithm ormodel by providing at least a portion of the virtual sensor data andcorresponding simulated conditions to train the machine learningalgorithm or model to determine one or more of a classification, aposition, and a dimension of the at least one object; and test themachine learning algorithm or model by providing at least a portion ofthe virtual sensor data to the machine learning algorithm or model todetermine one or more of a classification, a position, and a dimensionof the at least one object and by comparing a determined classification,a position, and a dimension of the at least one object with thesimulated conditions.

In Example 19, generating the virtual sensor data in any of Examples16-18 includes simulating the 3D environment by randomizing one or moreof the simulated conditions for one or more of the plurality ofpositions, wherein randomizing the one or more simulated conditionscomprises randomizing one or more of: lighting conditions; weatherconditions; a position of the one or more virtual objects; anddimensions of the one or more virtual objects.

In Example 20, determining the simulated conditions in any of Examples16-19 further includes generating a ground truth frame complimentary toa frame of virtual sensor data, wherein the ground truth frame comprisesa same color value for pixels corresponding to the at least one object.

Example 21 is a system or device that includes means for implementing amethod or realizing a system or apparatus as in any of Examples 1-20.

In the above disclosure, reference has been made to the accompanyingdrawings, which form a part hereof, and in which is shown by way ofillustration specific implementations in which the disclosure may bepracticed. It is understood that other implementations may be utilizedand structural changes may be made without departing from the scope ofthe present disclosure. References in the specification to “oneembodiment,” “an embodiment,” “an example embodiment,” etc., indicatethat the embodiment described may include a particular feature,structure, or characteristic, but every embodiment may not necessarilyinclude the particular feature, structure, or characteristic. Moreover,such phrases are not necessarily referring to the same embodiment.Further, when a particular feature, structure, or characteristic isdescribed in connection with an embodiment, it is submitted that it iswithin the knowledge of one skilled in the art to affect such feature,structure, or characteristic in connection with other embodimentswhether or not explicitly described.

As used herein, “autonomous vehicle” may be a vehicle that acts oroperates completely independent of a human driver; or may be a vehiclethat acts or operates independent of a human driver in some instanceswhile in other instances a human driver may be able to operate thevehicle; or may be a vehicle that is predominantly operated by a humandriver, but with the assistance of an automated driving/assistancesystem.

Implementations of the systems, devices, and methods disclosed hereinmay comprise or utilize a special purpose or general-purpose computerincluding computer hardware, such as, for example, one or moreprocessors and system memory, as discussed herein. Implementationswithin the scope of the present disclosure may also include physical andother computer-readable media for carrying or storingcomputer-executable instructions and/or data structures. Suchcomputer-readable media can be any available media that can be accessedby a general purpose or special purpose computer system.Computer-readable media that store computer-executable instructions arecomputer storage media (devices). Computer-readable media that carrycomputer-executable instructions are transmission media. Thus, by way ofexample, and not limitation, implementations of the disclosure cancomprise at least two distinctly different kinds of computer-readablemedia: computer storage media (devices) and transmission media.

Computer storage media (devices) includes RAM, ROM, EEPROM, CD-ROM,solid state drives (“SSDs”) (e.g., based on RAM), Flash memory,phase-change memory (“PCM”), other types of memory, other optical diskstorage, magnetic disk storage or other magnetic storage devices, or anyother medium which can be used to store desired program code means inthe form of computer-executable instructions or data structures andwhich can be accessed by a general purpose or special purpose computer.

An implementation of the devices, systems, and methods disclosed hereinmay communicate over a computer network. A “network” is defined as oneor more data links that enable the transport of electronic data betweencomputer systems and/or modules and/or other electronic devices. Wheninformation is transferred or provided over a network or anothercommunications connection (either hardwired, wireless, or a combinationof hardwired or wireless) to a computer, the computer properly views theconnection as a transmission medium. Transmissions media can include anetwork and/or data links, which can be used to carry desired programcode means in the form of computer-executable instructions or datastructures and which can be accessed by a general purpose or specialpurpose computer. Combinations of the above should also be includedwithin the scope of computer-readable media.

Computer-executable instructions comprise, for example, instructions anddata which, when executed at a processor, cause a general purposecomputer, special purpose computer, or special purpose processing deviceto perform a certain function or group of functions. The computerexecutable instructions may be, for example, binaries, intermediateformat instructions such as assembly language, or even source code.Although the subject matter has been described in language specific tostructural features and/or methodological acts, it is to be understoodthat the subject matter defined in the appended claims is notnecessarily limited to the described features or acts described above.Rather, the described features and acts are disclosed as example formsof implementing the claims.

Those skilled in the art will appreciate that the disclosure may bepracticed in network computing environments with many types of computersystem configurations, including, an in-dash vehicle computer, personalcomputers, desktop computers, laptop computers, message processors,hand-held devices, multi-processor systems, microprocessor-based orprogrammable consumer electronics, network PCs, minicomputers, mainframecomputers, mobile telephones, PDAs, tablets, pagers, routers, switches,various storage devices, and the like. The disclosure may also bepracticed in distributed system environments where local and remotecomputer systems, which are linked (either by hardwired data links,wireless data links, or by a combination of hardwired and wireless datalinks) through a network, both perform tasks. In a distributed systemenvironment, program modules may be located in both local and remotememory storage devices.

Further, where appropriate, functions described herein can be performedin one or more of: hardware, software, firmware, digital components, oranalog components. For example, one or more application specificintegrated circuits (ASICs) can be programmed to carry out one or moreof the systems and procedures described herein. Certain terms are usedthroughout the description and claims to refer to particular systemcomponents. As one skilled in the art will appreciate, components may bereferred to by different names. This document does not intend todistinguish between components that differ in name, but not function.

It should be noted that the sensor embodiments discussed above maycomprise computer hardware, software, firmware, or any combinationthereof to perform at least a portion of their functions. For example, asensor may include computer code configured to be executed in one ormore processors, and may include hardware logic/electrical circuitrycontrolled by the computer code. These example devices are providedherein purposes of illustration, and are not intended to be limiting.Embodiments of the present disclosure may be implemented in furthertypes of devices, as would be known to persons skilled in the relevantart(s).

At least some embodiments of the disclosure have been directed tocomputer program products comprising such logic (e.g., in the form ofsoftware) stored on any computer useable medium. Such software, whenexecuted in one or more data processing devices, causes a device tooperate as described herein.

While various embodiments of the present disclosure have been describedabove, it should be understood that they have been presented by way ofexample only, and not limitation. It will be apparent to persons skilledin the relevant art that various changes in form and detail can be madetherein without departing from the spirit and scope of the disclosure.Thus, the breadth and scope of the present disclosure should not belimited by any of the above-described exemplary embodiments, but shouldbe defined only in accordance with the following claims and theirequivalents. The foregoing description has been presented for thepurposes of illustration and description. It is not intended to beexhaustive or to limit the disclosure to the precise form disclosed.Many modifications and variations are possible in light of the aboveteaching. Further, it should be noted that any or all of theaforementioned alternate implementations may be used in any combinationdesired to form additional hybrid implementations of the disclosure.

Further, although specific implementations of the disclosure have beendescribed and illustrated, the disclosure is not to be limited to thespecific forms or arrangements of parts so described and illustrated.The scope of the disclosure is to be defined by the claims appendedhereto, any future claims submitted here and in different applications,and their equivalents.

What is claimed is:
 1. A method comprising: simulating, using one ormore processors, a three-dimensional (3D) environment comprising one ormore virtual objects; generating, using one or more processors, virtualsensor data for a plurality of positions of one or more sensors withinthe 3D environment; determining, using one or more processors, virtualground truth corresponding to each of the plurality of positions,wherein the ground truth comprises a dimension or parameter of the oneor more virtual objects; and storing and associating the virtual sensordata and the virtual ground truth using one or more processors.
 2. Themethod of claim 1, further comprising providing one or more of thevirtual sensor data and the virtual ground truth for training or testingof a machine learning algorithm or model.
 3. The method of claim 2,wherein the machine learning model or algorithm comprises a neuralnetwork.
 4. The method of claim 2, wherein training the machine learningalgorithm or model comprises providing at least a portion of the virtualsensor data and corresponding virtual ground truth to train the machinelearning algorithm or model to determine one or more of a dimension or aparameter of the one or more virtual objects.
 5. The method of claim 2,wherein testing the machine learning algorithm or model comprisesproviding at least a portion of the virtual sensor data to the machinelearning algorithm or model to determine one or more of a dimension or aparameter of the one or more virtual objects and compare a determineddimension or a determined parameter with the virtual ground truth. 6.The method of claim 1, wherein the plurality of positions correspond toplanned locations of sensors on a vehicle.
 7. The method of claim 1,wherein the virtual sensor data comprises one or more of computergenerated images, computer generated radar data, computer generatedLIDAR data, and computer generated ultrasound data.
 8. The method ofclaim 1, wherein simulating the 3D environment comprises randomlygenerating different conditions for one or more of lighting, weather, aposition of the one or more virtual objects, and dimensions of the oneor more virtual objects.
 9. The method of claim 1, wherein generatingthe virtual sensor data comprises periodically generating the virtualsensor data during simulated movement of the one or more sensors withinthe 3D environment.
 10. The method of claim 1, wherein determining thevirtual ground truth comprises generating a ground truth framecomplimentary to a frame of virtual sensor data, wherein the groundtruth frame comprises a same color value for pixels corresponding to theone or more virtual objects.
 11. A system comprising: an environmentcomponent configured to simulate, using one or more processors, athree-dimensional (3D) environment comprising one or more virtualobjects; a virtual sensor component configured to generate, using one ormore processors, virtual sensor data for a plurality of positions of oneor more sensors within the 3D environment; a ground truth componentconfigured to determine virtual ground truth corresponding to each ofthe plurality of positions, wherein the ground truth comprises adimension or parameter of the at least one of the virtual objects; and amodel component configured to provide the virtual perception data andthe ground truth to a machine learning model or algorithm to train ortest the machine learning model or algorithm using one or moreprocessors.
 12. The system of claim 11, wherein the model component isconfigured to train the machine learning algorithm or model, whereintraining comprises: providing at least a portion of the virtual sensordata and corresponding virtual ground truth to train the machinelearning algorithm or model to determine one or more of a dimension or aparameter of the one or more virtual objects.
 13. The system of claim11, wherein the model component is configured to test the machinelearning algorithm or model, wherein testing comprises: providing atleast a portion of the virtual sensor data to the machine learningalgorithm or model to determine one or more of a dimension or a heightof the one or more virtual objects; and comparing a determined dimensionor a determined parameter with the virtual ground truth.
 14. The systemof claim 11, wherein the virtual sensor component is configured togenerate virtual sensor data comprising one or more of computergenerated images, computer generated radar data, computer generatedlight detection and ranging (LIDAR) data, and computer generatedultrasound data.
 15. The system of claim 11, wherein the environmentcomponent is configured to simulate the 3D environment by randomlygenerating different conditions for one or more of the plurality ofpositions, wherein the different conditions comprise one or more of:lighting conditions; weather conditions; a position of the one or morevirtual objects; and dimensions of the one or more virtual objects. 16.Non-transitory computer readable storage media storing instructionsthat, when executed by one or more processors, cause the one or moreprocessors to: generate virtual sensor data for a plurality of sensorpositions within a simulated three-dimensional (3D) environmentcomprising one or more virtual objects; determine one or more simulatedconditions for each of the plurality of positions, wherein the simulatedconditions comprise one or more of a dimension of the at least one ofthe virtual objects and a position of the virtual objects in relation toa virtual sensor; and store and annotate the virtual sensor data withthe simulated conditions.
 17. The computer readable storage of claim 16,wherein the instructions further cause the one or more processors totrain or test a machine learning algorithm or model based on one or moreof the virtual sensor data and the simulated conditions.
 18. Thecomputer readable storage of claim 17, wherein one or more of: theinstructions cause the one or more processors to train the machinelearning algorithm or model by providing at least a portion of thevirtual sensor data and corresponding simulated conditions to train themachine learning algorithm or model to determine one or more of adimension or a position of the one or more virtual objects; and theinstructions cause the one or more processors to test the machinelearning algorithm or model by providing at least a portion of thevirtual sensor data to the machine learning algorithm or model todetermine one or more of a dimension and a position of the virtualobjects and by comparing a determined dimension or a determined positionwith the simulated conditions.
 19. The computer readable storage ofclaim 16, wherein generating the virtual sensor data comprisessimulating the 3D environment by randomizing one or more of thesimulated conditions for one or more of the plurality of positions,wherein randomizing the one or more simulated conditions comprisesrandomizing one or more of: lighting conditions; weather conditions; aposition of the one or more virtual objects; and dimensions of the oneor more virtual objects.
 20. The computer readable storage of claim 16,wherein determining the simulated conditions further comprisesgenerating a ground truth frame complimentary to a frame of virtualsensor data, wherein the ground truth frame comprises a same color valuefor pixels corresponding to the one or more virtual objects.